• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.167-178
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• 2001

Key is generally formed using the Random Number. How to make the Random Number is to cast coin or dice as classical method, to form the Real Random Number with Hardware and to make the Pseudo Random Number by means of utilizing mathematical algorithm. This thesis presented NRNG(New Random Number Generator) which put self-development Hardware to use as Key Generation Method and inspected to compare the Real Random Number with the Pseudo Random Number and special properties which PRNG(Pseudo-Random Number Generator) creates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.655-657
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• 2008

We suggest the digital voice encryption module using the pseudo random number generator and design the sorority device of a telephone using the module. The proposed method provides encryption method of the telephone against the third party. This encryption method uses pseudo random number generator which computes the encryption key using the shared secret key and the current time value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1006-1009
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• 2008

We suggest the digital voice encryption module using the pseudo random number generator and design the security device of a telephone using the module. The proposed method provides encryption method of the telephone against the third party. This encryption method uses pseudo random number generator which computes the encryption key using the shared secret key and the current time value.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.878-881
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• 2009

As for the technology developed for network security, there is little difference of design ability between the domestic and the foreign studies. Although the development of 2048 RSA processor has been undergone, the processing speed does not meet the requirement due to its long width. These days, an RSA processor architecture with higher speed comsuming less resource is necessary. As for the development of RNG (Random Number Generator), the technology trend is moving from PRNG (Pseudo Random Number Generator) to TRNG (True Random Number Generator), also requiring less area and high speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.803-806
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• 2007

Since the different characteristics from the PRNG (Pseudo Random Number Generator) or various deterministic devices such as arithmetic processing units, new concepts and test methods should be suggested in order to test TRNG (Ture Random Number Generator). Deterministic devices can be covered by ATPG (Automatic Test Pattern Generation), which uses patterns generated by cyclic shift registers due to its hardware oriented characteristics, pure random numbers are not possibly tested by automatic test pattern generation due to its analog-oriented characteristics. In this paper, we studied and analyzed a hardware/software combined test method named Diehard test, in which we apply continuous pattern variation to check the statistics. We also point out the considerations when making random number tests.

• ETRI Journal
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• v.42 no.4
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• pp.518-526
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• 2020

This paper presents an efficient hardware random-number generator based on a beta source. The proposed generator counts the values of "0" and "1" and provides a method to distinguish between pseudo-random and true random numbers by comparing them using simple cumulative operations. The random-number generator produces labeled data indicating whether the count value is a pseudo- or true random number according to its bit value based on the generated labeling data. The proposed method is verified using a system based on Verilog RTL coding and LabVIEW for hardware implementation. The generated random numbers were tested according to the NIST SP 800-22 and SP 800-90B standards, and they satisfied the test items specified in the standard. Furthermore, the hardware is efficient and can be used for security, artificial intelligence, and Internet of Things applications in real time.

• Journal of KIISE
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• v.42 no.12
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• pp.1467-1473
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• 2015

In this paper, we implemented a parallel random number generation program on GPU's, which are known for high performance computing, using LCG (Linear Congruential Generator). Random numbers are important in all fields requiring the use of randomness, and LCG is one of the most widely used methods for the generation of pseudo-random numbers. We explained the parallel program using the NVIDIA CUDA model and MPI(Message Passing Interface) and showed uniform distribution and performance results. We also used a Monte Carlo algorithm to calculate pi(${\pi}$) comparing the parallel random number generator with cuRAND, which is a CUDA library function, and showed that our program is much more efficient. Finally we compared performance results using multi-GPU's with those of ideal speedups.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.237-246
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• 1998

We put the pseudo-random number generator into catagories like MiCG, MuCG, URG, ICG, EICG, and test uniformity and independency by 10,000 times through n empirical trial after selecting this random number generator. Here, from a fraction of data(20, 40, 60, 80, 100) with a significance level of 0.1, 0.05 and 0.01, we drive cumulative frequency with K-S, $X^{2}$, poker, run, autocorrelation test. As a result from the uniformity and independency among five random number generators based on all these data, all random number generator except EICG passed uniformity and independency test, and the URG turn out to be excellent in periodicity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6161-6176
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• 2018

Pseudorandom numbers are useful in cryptographic operations for using as nonce, initial vector, secret key, etc. Security of the cryptosystem relies on the secret key parameters, so a good pseudorandom number is needed. In this paper, we have proposed a new approach for generation of pseudorandom number. This method uses the three dimensional combinational puzzle Rubik Cube for generation of random numbers. The number of possible combinations of the cube approximates to 43 quintillion. The large possible combination of the cube increases the complexity of brute force attack on the generator. The generator uses cryptographic hash function. Chaotic map is being employed for increasing random behavior. The pseudorandom sequence generated can be used for cryptographic applications. The generated sequences are tested for randomness using NIST Statistical Test Suite and other testing methods. The result of the tests and analysis proves that the generated sequences are random.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.676-682
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• 2018

As the IoT world including WSNs develops, the number of sensor systems that sense information according to the environment based on the principle of IoT is increasing. In order to perform security for each sensor system in such a complicated environment, the security modules must be varied. These problems make hardware/software implementation difficult when considering the system efficiency and hacking/cracking. Therefore, to solve this problem, this paper proposes a pseudorandom number generator (FLT: Pseudo-random Number Generator with Fixed Length Tap unrelated to the variable sensing nodes) with a fixed-length tap that generates a pseudorandom number with a constant period, irrespective of the number of sensing nodes, and has the purpose of detecting anomalies. The proposed FLT-LFSR architecture allows the security level and overall data formatting to be kept constant for hardware/software implementations in an IoT environment. Therefore, the proposed FLT-LFSR architecture emphasizes the scalability of the network, regardless of the ease of implementation of the sensor system and the number of sensing nodes.